Extending Association Rule Summarization Techniques to Assess Risk of Diabetes Mellitus

Abstract

The main aim of this project is to predict the excess risk of diabetes for the patients and summarize their sub population by using Association Rule Mining. In Data Mining, association rule learning is a popular and well researched method for discovering interesting relations between variables in large databases. To Apply Association Rule Mining to electronic medical records (EMR) to discover sets of risk factors and their corresponding subpopulations that represent patients at particularly high risk of developing diabetes. An Electronic Medical Record (EMR) is an evolving concept defined as a systematic collection of electronic health information about individual patients or population. The high dimensional of EMR’s, association rule mining generates a very large set of rules which we need to summarize for easy clinical use. Applied four association rule set stigmatization techniques and conducted a comparative evaluation to provide guidance regarding their applicability, strengths and weaknesses. We found that all four methods produced summaries that described sub populations at high risk of diabetes with each method having its clear strength. For our purpose, our extension to the Bottom-Up Stigmatization (BuS) is the best practice in the entire above summary.